The feasibility of a subgroup approach to create sensory-motor behavioral response profiles from a modified quantitative sensory test in cerebral palsy
Bibliographic record
Abstract
ABSTRACT: Cerebral palsy is the most common motor disorder of childhood, leading to lifelong disability and associated comorbidities, including a high incidence of chronic pain. This hypothesis-generating study aimed to (1) investigate a modeling approach to subgrouping based on behavioral reactivity (BR) to a modified quantitative sensory test (mQST), and (2) explore potential relations between BR subgroups, pain status, and clinical features. For this cross-sectional study, recruitment was conducted through a systematic proportional stratified sampling approach in relation to cerebral palsy type (eg, spastic, mixed type). Caregivers completed the Dalhousie Pain Interview; health history was collected from the medical record. The mQST included 6 tactile stimuli applied to the back of the right and left calves (eg, light touch, pressure, heat), and the BR associated with each application was scored using a modified Face Legs, Activity, Cry, Consolability scale. Of the 188 participants enrolled, 12 (6.4%) did not tolerate the full mQST. Latent class analysis was conducted with 172 participants, and the model with 6 classes had the best fit, with no classes having fewer than 5% of the sample. The 6-class solution revealed subgroups with relatively flat to relatively reactive patterns specific to different stimulus application modalities, providing sufficient evidence that reactivity subgroups can be reliably modeled. Descriptively, there were no apparent differences in the BR profile by sex; however, potentially by age and pain experience. Our results in this study indicate that sensory response profiles may be a promising approach to better understand the nature of chronic pain in cerebral palsy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.027 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".